Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …

In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

Increased interest in the opportunities provided by artificial intelligence and machine
learning has spawned a new field of health-care research. The new tools under …

Abstract Machine learning (ML) is becoming an integral aspect of several domains in
medicine. Yet, most pathologists and laboratory professionals remain unfamiliar with such …

State-of-the-art decision tree methods apply heuristics recursively to create each split in
isolation, which may not capture well the underlying characteristics of the dataset. The …

In any given machine learning problem, there may be many models that could explain the
data almost equally well. However, most learning algorithms return only one of these …

The problem of adversarial robustness has been studied extensively for neural networks.
However, for boosted decision trees and decision stumps there are almost no results, even …

The analysis of infrared spectroscopy of substances is a non-invasive measurement
technique that can be used in analytics. Although the main objective of this study is to …

This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …